"""Hold sample data from which we will calculate the test stat.

"""

def __init__(self, *args, **kwargs):

# tuple.__init__(self, *args, **kwargs)

tuple.__init__(self)

def __mul__(self, scalar):

"""Multiply a sample by a scalar.

eg 0.5*(1,1,1,1,2,1) = (0.5,0.5,0.5,0.5,1.0,0.5)

"""

# BAD python 2.3 compat change

return type(self)( [scalar*item for item in self] )

def __add__(self, other):

"""Add two samples together pointwise.

eg (0,0,0,0,0,0) + (1,1,1,1,2,1) = (1,1,1,1,2,1)

"""

if len(self) != len(other):

raise ValueError, "Can only add samples of the same length."

"""Store a dictionary of samples.

This is generally used to store all of the samples for a region, categorized

by their named partitions. An example of this would be looking at an entire,

genome, but having it partitioned by chromosome. The keys would be the chr

names.

"""

def __setitem__(self, key, item):

#assert isinstance(item, sample)

dict.__setitem__(self, key, item)

def sum(self):

"""Returns the sum of all the overlap_sample objects.

In example, if the values in this container were

(0,0,0,0,2,4)

and (1,4,5,7,2,4)

this would return (1,4,5,7,4,8).

"""

if len(self) == 0:

raise ValueError, "Can't sum an empty sequence"

# define a container to hold the values

empty = type(self.values()[0])(0,0,0,0,0,0)

# iterate through the items, and add them to the empty container

for item in self.values():

empty += item

return empty

def weightedSum(self, weights):

"""Returns the sum of all overlap_sample objects weighted by weights.

In example, if the values in this container were

'R1': (0,0,0,0,2,4)

and 'R2': (1,4,5,7,2,4)

and the weights were

'R1': 0.1

and 'R2': 0.9

then this would return

(0, 0, 0, 0, .2, .4) + (0.9, 3.6, 4.5, 6.3, 1.8, 3.6)

= (0.9, 3.6, 4.5, 6.3, 2.0, 4.0)

"""

if len(self) == 0:

raise ValueError, "Can't sum an empty sequence"

# make sure that the keys are identical

assert set(weights.keys()) == set(self.keys())

# make sure that the weights sum to 1

assert round( sum( weights.values() ), 15) == 1.0

# define a container to hold the values

obj = self.values()[0]

obj_type = type(obj)

if obj_type in (int,float):

empty = obj_type(0)

else:

empty = obj_type(args=[0]*len(obj))

# BAD python2.3 compatability change

outer_regionFraction, inner_regionFraction,

callback, number=1 ):

"""Double sample from a pair of regions and calculate aggregate stats.

This is a wrapper for custom GSC statistics. This code samples from the

regions with the specified region fractions, and then calls callback with

s11,s12, s21, s22 for the ouer and inner samples. The callback should return

an object inherited from sample. Then, double overlap sample will return

a sample dict containing the samples hashed by region name.

It's probably easiest to quickly browse the code and an example.

"""

osl = outer_regionFraction

isl = inner_regionFraction

# for loop in the number of samples to take

for loop in xrange(number):

# first, build a dict of the outer samples

# these are what we use to calculate the expectation

# of the statistic

osample = sample_dict()

isample = sample_dict()

# for each named region in the coveringRegion...

for key in coveringRegion.keys():

# rename the regions to be more readable

cA = coveringRegion[key]

eA = coveredRegion[key]

if cA.length != eA.length:

raise ValueError, "Regions must be the same length in bp's."

###############################

# Take the outer samples

###############################

# find the length of the sample in bp's

sample_length = int(eA.length*osl)

# take the slices

start_bp = randint(0, cA.length - sample_length - 1)

os1 = eA.get_subregion( start_bp, start_bp+sample_length, shift_to_zero=True )

start_bp = randint(0, cA.length - sample_length - 1)

os2 = cA.get_subregion( start_bp, start_bp+sample_length, shift_to_zero=True )

# save the outer sample stat

osample[key] = callback( os1, os2 )

###############################

# Take the (inner) sub samples

###############################

# find the length of the sample in bp's

sample_length = int(sample_length*isl)

# take the slices

start_bp = randint(0, os1.length - sample_length - 1)

is1 = eA.get_subregion( start_bp, start_bp+sample_length, shift_to_zero=True )

start_bp = randint(0, os1.length - sample_length - 1)

is2 = cA.get_subregion( start_bp, start_bp+sample_length, shift_to_zero=True )

# save the inner sample stat

isample[key] = callback( is1, is2 )

regionFraction, callback, number=1 ):

"""Block sample from a pair of regions and calculate aggregate stats.

This is a wrapper for custom GSC statistics. This code samples from the

regions with the specified region fractions, and then calls callback with

s11,s12, s21, s22 for the otuer and inner samples. The callback should return

an object inherited from sample. Then, single overlap sample will return

a sample dict containing the samples hashed by region name.

It's probably easiest to quickly browse the code and an example - correlation

is a good placde to start.

"""

sl = regionFraction

# for loop in the number of samples to take

for loop in xrange(number):

sample = sample_dict()

# for each named region in the coveringRegion...

for key in coveringRegion.keys():

# rename the regions to be more readable

cA = coveringRegion[key]

eA = coveredRegion[key]

if cA.length != eA.length:

raise ValueError, "Regions must be the same length in bp's."

###############################

# Take the outer samples

###############################

# find the length of the sample in bp's

sample_length = int(eA.length*sl)

# take the slices

start_bp = randint(0, cA.length - sample_length - 1)

s1 = eA.get_subregion( start_bp, start_bp+sample_length, shift_to_zero=True )

start_bp = randint(0, cA.length - sample_length - 1)

s2 = cA.get_subregion( start_bp, start_bp+sample_length, shift_to_zero=True )

# save the outer sample stat

sample[key] = callback( s1, s2 )

covering_region, covered_region, region_fraction, num_samples ):

#% Lastly, scale the bootstrap distribution to get the null distribution of

#% the test statistic.

#

#% 2*frac = 2L/n. So sqrt(2*frac)*T_n has the correct SD. We can compute

#% this by pulling the constant out:

#

#% store the samples from n and N

#%SD = sqrt(2*frac)*std(Tn);

#

Tns = []

Tns_2 = []

# calculate the overlap stat num_samples times

samples = random_regions_bp_overlap(covered_region, covering_region, region_fraction, num_samples)

# BAD python2.3 compat change

lengths = dict([ (key, covered_region[key].length) for key in covered_region.keys() ])

if verbose:

print >> output, "#### Sample Distribution Info"

print >> output, "Sample #".rjust(8), "Tn".rjust(15)

loop = 0

for sample in samples:

stats = conditional_overlap_sample_stat(sample, lengths, region_fraction)

Tns.append(stats['Tn'])

if verbose: print >> output, str(loop).rjust(8), str("%.8f" % stats['Tn']).rjust(15)

loop += 1

SD = sqrt(2*region_fraction)*std(Tns)

if verbose:

print >> output

print >> output, 'mean: ', mean(Tns)

print >> output, 'SD: ', SD, '\n'

real_stats = conditional_overlap_stat(covered_region, covering_region)

theoretical_stat_mean = real_stats['theoretical']

observed_stat_mean = real_stats['observed']

test_stat = real_stats['test_stat']

if verbose:

print >> output, 'NULL stat mean: ', theoretical_stat_mean

print >> output, 'observed stat mean: ', observed_stat_mean

print >> output, 'test_stat: ', test_stat

# Finally, refer the "mean zero" test statistic "test_stat" to the

# distribution Tn. Compute the SD and whatnot.

#

#%z_score = test_stat/SD;

#%p_value = 1 - normcdf(test_stat,0,SD);

z_score = test_stat/SD

if verbose: print >> output, 'z_score: ', z_score

p_value = min(1 - sn_cdf(z_score), sn_cdf(z_score))

print >> output, 'p_value: ', p_value, "\n"

outer_regionFraction, inner_regionFraction,

aggregate_sample_callback,

regions_agg_callback,

nsamples=1,

real_stat_callback=None ):

"""Wrapper for generic double bootstrap stat

Input Parameters:

covering_regions - the regions that num regions is calulated from

covered_regions - the other regions

outer_region_fraction - the ratio of the outer sample

inner_region_fraction - the ratio of the subsample to the outer sample

aggregate_samples_callback: this is what is called on the block samples

that are taken. the quintesential example of this is region overlap.

scale_sample_callback: scales the stat that was aggregated from

aggregate_samples_callback.

regions_agg_callback: How to aggregate the samples. The two implemented

are the conditional and marginal version. Marginal adds up all of the

segments - the conditional weights them by the segment weights.

nsamples - the number of samples to take

real_stat_callback - sometimes we need to calculate the 'true' stat

differently than for the samples ( for instance, maybe it needsw to

be scaled differently. In such cases, pass this in. If it is none, the

real stat is calculated by calling the sample functions on the whole

set of regions.

"""

# store the samples

o_samples = []

i_samples = []

if verbose:

# TODO make this prettier, maybe with a sample header method?

print >> output, "#### Samples\n"

print >> output, "Sample #".rjust(8), "Outer Region Stat".rjust(23), \

"Inner Region Stat".rjust(23)

# take the samples and keep track of the relevant data

for loop, item in \

enumerate(double_overlap(

covered_regions, covering_regions,

outer_regionFraction, inner_regionFraction,

aggregate_sample_callback, nsamples

)):

# unpack the samples tuple

outer_samples, inner_samples = item

# append the aggregated stats, should be a floats

o_samples.append(regions_agg_callback(outer_samples))

i_samples.append(regions_agg_callback(inner_samples))

#print outer_samples, inner_samples

#print agg_callback(outer_samples), agg_callback(inner_samples)

if verbose:

print >> output, str(loop+1).rjust(8), \

str(o_samples[-1]).rjust(23), str(i_samples[-1]).rjust(23)

# the expectation of the mean under the NULL

# our estimate is the empirical mean of the outer sample

Nmean = mean(o_samples)

obsMean = mean(i_samples)

if rpy != None and verbose:

rpy.r.png( "dist.png", width=600, height=600 )

rpy.r.par( mfrow=(2,2) )

rpy.r.hist( o_samples, main="Outer Sample", xlab="", ylab="" )

rpy.r.hist( i_samples, main="Inner Sample", xlab="", ylab="" )

rpy.r.qqnorm( o_samples, main="Outer Sample", xlab="", ylab="" )

rpy.r.qqline( o_samples )

rpy.r.qqnorm( i_samples, main="Inner Sample", xlab="", ylab="" )

rpy.r.qqline( i_samples )

rpy.r.dev_off()

dist = [ (sample - Nmean) for sample in i_samples ]

dist_sd = sqrt(outer_regionFraction*inner_regionFraction)*std(dist)

if verbose: print >> output, "\nSample Dist Info:\n"

if verbose: print >> output, "\tThe expected mean under the NULL: ", Nmean

if verbose: print >> output, "\tThe sample mean: ", obsMean

if verbose: print >> output, "\tThe normalized bootstrap SD: ", dist_sd, "\n"

# the stat on each of the segments

if None == real_stat_callback:

seg_stats = sample_dict([ (key, aggregate_sample_callback( covered_regions[key], covering_regions[key] )) for key in covered_regions.keys() ])

real_stat_expectation = regions_agg_callback( seg_stats )

else:

real_stat_expectation = real_stat_callback( covered_regions, covered_regions )

obs_mean = real_stat_expectation - Nmean

if verbose: print >> output, "\tThe real stat: ", real_stat_expectation

if verbose: print >> output, "\tThe scaled real stat: ", obs_mean, "\n"

z_score = obs_mean/dist_sd

print "\tZ Score: ", z_score

p_value = min( 1 - sn_cdf( z_score ), sn_cdf( z_score ) )

print "\tp-value: ", p_value

regionFraction,

exp_under_null_callback,

aggregate_sample_callback,

regions_agg_callback,

nsamples=1,

real_stat_callback=None):

"""Wrapper for generic double bootstrap stat

Input Parameters:

covering_regions - the regions that num regions is calulated from

covered_regions - the other regions

region_fraction - the fraction of the region to sample

exp_under_null_callback - determine the expectation of the stat under

the null, as a function of the covering and covered regions.

aggregate_samples_callback: this is what is called on the block samples

that are taken. the quintesential example of this is region overlap.

scale_sample_callback: scales the stat that was aggregated from

aggregate_samples_callback.

regions_agg_callback: How to aggregate the samples. The two implemented

are the conditional and marginal version. Marginal adds up all of the

segments - the conditional weights them by the segment weights.

nsamples - the number of samples to take

"""

# store the samples

samples = []

if verbose:

# TODO make this prettier, maybe with a sample header method?

print >> output, "#### Samples\n"

print >> output, "Sample #".rjust(8), "Stat".rjust(23)

# take the samples and keep track of the relevant data

for loop, sample in \

enumerate(single_overlap(

covered_regions, covering_regions,

regionFraction,

aggregate_sample_callback, nsamples

)):

# append the aggregated stats, should be a floats

samples.append(regions_agg_callback(sample))

#print outer_samples, inner_samples

#print agg_callback(outer_samples), agg_callback(inner_samples)

if verbose:

print >> output, str(loop+1).rjust(8), \

str(samples[-1]).rjust(23)

# the expectation of the mean under the NULL

# our estimate is the empirical mean of the outer sample

Nmean = exp_under_null_callback( covering_regions, covering_regions )

obsMean = mean(samples)

if rpy != None and verbose:

rpy.r.png( "dist.png", width=1200, height=600 )

rpy.r.par( mfrow=(1,2) )

rpy.r.hist( samples, main="Sample Hist", xlab="", ylab="", n=nsamples/10.0 )

rpy.r.qqnorm( samples, main="qqplot vs norm quantiles", xlab="", ylab="" )

rpy.r.qqline( samples )

rpy.r.dev_off()

dist = [ (sample - Nmean) for sample in samples ]

dist_sd = sqrt(regionFraction)*std(dist)

if verbose: print >> output, "\nSample Dist Info:\n"

if verbose: print >> output, "\tThe expected mean under the NULL: ", Nmean

if verbose: print >> output, "\tThe sample mean: ", obsMean

if verbose: print >> output, "\tThe normalized bootstrap SD: ", dist_sd, "\n"

# the stat on each of the segments

# the stat on each of the segments

if None == real_stat_callback:

seg_stats = sample_dict([ (key, aggregate_sample_callback( covered_regions[key], covering_regions[key] )) for key in covered_regions.keys() ])

real_stat_expectation = regions_agg_callback( seg_stats )

else:

real_stat_expectation = real_stat_callback( covered_regions, covering_regions )

obs_mean = real_stat_expectation - Nmean

if verbose: print >> output, "\tThe real stat: ", real_stat_expectation

if verbose: print >> output, "\tThe scaled real stat: ", obs_mean, "\n"

z_score = obs_mean/dist_sd

print "\tZ Score: ", z_score

p_value = min( 1 - sn_cdf( z_score ), sn_cdf( z_score ) )

print "\tp-value: ", p_value

""" Calculate region overlap statistics.

This is intended as a callback for single_overlap.

"""

# stores the feature length of the covered region ( self )

covered_feature_len = coveredRegionSample.featuresLength()

# stores the feature length of the covering region ( coveringRegion )

covering_feature_len = coveringRegionSample.featuresLength()

# stores the observed total feature overlap between the region's

overlap_feature_len = coveredRegionSample.overlap(coveringRegionSample)

covering_regions, covered_regions, \

region_fraction, num_samples ):

def agg_callback(sample):

total_length = sum( item.length for item in covering_regions.values() )

Fn = 0.0

Jn_num = 0.0

Jn_denom = 0.0

for r_name, values in sample.iteritems():

# unpack the sample values

overlap, covered_feature_len, covering_feature_len = values

regionLength = float(covering_regions[r_name].length)

# lambda in the paper

length_frac = regionLength/total_length

# the length of the sampled region

sampleRegionLength = region_fraction*regionLength

I = covered_feature_len/sampleRegionLength

J = covering_feature_len/sampleRegionLength

IJ = overlap/sampleRegionLength

Fn += length_frac*(IJ/max(I, 1.0/sampleRegionLength))

Jn_denom += length_frac*I

Jn_num += length_frac*I*J

return Fn

def analytical_expectation( covering_regions, covered_regions ):

totalLength = sum( item.length for item in covering_regions.values() )

Obs_num = 0.0

Obs_den = 0.0

I_num = 0.0

for key in covered_regions.keys():

covered_feature_len = covered_regions[key].featuresLength()

covering_feature_len = covering_regions[key].featuresLength()

overlap_feature_len = covered_regions[key].overlap(covering_regions[key])

# stores the length of this region for both features

region_length = float(covered_regions[key].length)

# this is lambda_i in the paper

regionFraction = region_length/totalLength

Obs_num += regionFraction*overlap_feature_len/region_length

Obs_den += regionFraction*covered_feature_len/region_length

I_num += regionFraction*covering_feature_len*covered_feature_len/(region_length**2)

J_n = I_num/Obs_den;

O_n = Obs_num/Obs_den;

return { 'expected': J_n, 'observed': O_n }

def expectation_under_null( covering_regions, covered_regions ):

return analytical_expectation( covering_regions, covered_regions )[ 'expected' ]

def real_stat( covering_regions, covered_regions ):

return analytical_expectation( covering_regions, covered_regions )[ 'observed' ]

z_score, p_value = \

single_bootstrap_stat( covering_regions, covered_regions, \

region_fraction, expectation_under_null, \

fraction_basepair_overlap, agg_callback, \

num_samples, real_stat )

covering_regions, covered_regions, \

region_fraction, num_samples ):

weights = covering_regions.regionFraction()

def agg_callback(sample):

assert set( sample.keys() ) == set( covering_regions.keys() )

assert set( sample.keys() ) == set( covered_regions.keys() )

tot_overlap = 0

tot_fl = 0

for r_name, values in sample.iteritems():

overlap_feature_len, covered_feature_len, covering_feature_len = values

tot_overlap += float(overlap_feature_len)

tot_fl += covered_feature_len

# If we want to do this conditional on features, uncomment this

"""

if tot_fl == 0:

return 0

else:

return tot_overlap/tot_fl

"""

return tot_overlap/tot_fl

def expectation_under_null( covering_regions, covered_regions ):

covered_fl = 0

covering_fl = 0

region_lengths = 0

for r_name in covering_regions.keys():

covered_fl += covered_regions[r_name].featuresLength()

covering_fl += covering_regions[r_name].featuresLength()

region_lengths += covered_regions[r_name].length

# Note that this is really (covering*covered/rl**2)/covered,

# which reduces algebraically to the below expression

return covering_fl/float(region_lengths)

z_score, p_value = \

single_bootstrap_stat( covering_regions, covered_regions, \

region_fraction, expectation_under_null, \

fraction_basepair_overlap, agg_callback, \

num_samples )

covering_regions, covered_regions,

outer_regionFraction, inner_regionFraction,

nsamples, min_overlap_fraction=0.0

):

"""Calculate the probability under the NULL of independence that the region

overlap is purely do to chance.

I use the machinery in double bootstrap stat to implement this.

"""

# the region weights - defined as homogenous region length/ total length

weights = covering_regions.regionFraction()

def agg_callback(sample):

assert set( sample.keys() ) == set( covering_regions.keys() )

assert set( sample.keys() ) == set( covered_regions.keys() )

return sample.weightedSum( weights )

def region_overlap( coveredRegionSample, coveringRegionSample ):

""" Calculate region overlap statistics.

This is intended as a callback for double_overlap.

"""

percent_overlap = float(coveredRegionSample.regionOverlap(coveringRegionSample, min_overlap_fraction)) / coveredRegionSample.numRegions

return percent_overlap

return double_bootstrap_stat(covering_regions, covered_regions,

outer_regionFraction, inner_regionFraction,

region_overlap, agg_callback,

covering_regions, covered_regions,

outer_regionFraction, inner_regionFraction,

nsamples, min_overlap_fraction=0.0

):

"""Calculate the probability under the NULL of independence that the region

overlap is purely do to chance.

I use the machinery in double bootstrap stat to implement this.

"""

def agg_callback(sample):

assert set( sample.keys() ) == set( covering_regions.keys() )

assert set( sample.keys() ) == set( covered_regions.keys() )

overlaps = sum([ item[0] for item in sample.values() ])

num_regions = sum([ item[1] for item in sample.values() ])

return float(overlaps)/num_regions

def region_overlap( coveredRegionSample, coveringRegionSample ):

""" Calculate region overlap statistics.

This is intended as a callback for double_overlap.

"""

region_overlap = ( float(coveredRegionSample.regionOverlap(coveringRegionSample, min_overlap_fraction)), coveredRegionSample.numRegions )

return region_overlap

return double_bootstrap_stat(covering_regions, covered_regions,

outer_regionFraction, inner_regionFraction,

region_overlap, agg_callback,

covering_regions, covered_regions,

regionFraction, nsamples

):

"""Calculate the probability under the NULL of independence that the pearson

correlation is purely do to chance.

I use the machinery in double bootstrap stat to implement this.

"""

def agg_callback(sample):

"""Correlation is asmytotically normal, so we weight the correlation

by 1/sqrt(region length), since length is the number of

points that contribute to the correlation.

"""

stat = 0

for key, val in sample.iteritems():

length = covering_regions[key].length

stat += val/sqrt( length )

return stat

def pearson_correlation( coveredRegionSample, coveringRegionSample ):

""" Calculate region overlap statistics.

This is intended as a callback for double_overlap.

"""

correlation = coveredRegionSample.regionCorrelation(coveringRegionSample)

return correlation

def mean_under_null( covering_regions, covered_regions ):

return 0.0

return single_bootstrap_stat(covering_regions, covered_regions,

regionFraction,

mean_under_null,

pearson_correlation,

agg_callback,

covering_regions, covered_regions,

outer_regionFraction, inner_regionFraction,

nsamples

):

"""Calculate the probability under the NULL of independence that the fold

of region 1 WRT region2 is due to chance.

I use the machinery in double bootstrap stat to implement this.

"""

# the region weights - defined as homogenous region length/ total length

weights = covering_regions.regionFraction()

def agg_callback(sample):

assert set( sample.keys() ) == set( covering_regions.keys() )

assert set( sample.keys() ) == set( covered_regions.keys() )

return sample.weightedSum( weights )

def fold( coveredRegionSample, coveringRegionSample ):

# special case for empty intervals

if len(coveredRegionSample) == 0 or len(coveringRegionSample) == 0: return 0

totalEnrichment = 0

currentMatches = []

thisIter = coveredRegionSample.iter_intervals_and_values()

nextCoveredMatch = thisIter.next()

for cg_iv, cg_val in coveringRegionSample.iter_intervals_and_values():

# first, get rid of any current matches that dont match

# because of the ordering, these should start not matching at the begining

for cd_iv, cd_val in currentMatches:

if cd_iv.end >= cg_iv.start: break

else: del currentMatches[0]

# next, add any new items to currentMatches

while nextCoveredMatch != None and nextCoveredMatch[0].start <= cg_iv.end:

currentMatches.append(nextCoveredMatch)

try: nextCoveredMatch = thisIter.next()

except StopIteration: nextCoveredMatch = None

# finally, calculate the fold enrivchment of every item in currentMatches

for cd_iv, cd_val in currentMatches:

totalEnrichment += cd_iv.overlap(cg_iv)*( float(cg_val)/cd_val )

return totalEnrichment/coveredRegionSample.featuresLength()

return double_bootstrap_stat( covering_regions, covered_regions,

outer_regionFraction, inner_regionFraction,

fold, agg_callback,

print >> output, """

This calculates a p-value under the null hypothesis that the observed

instance-coverage of Feature_1 by Feature_2 is due to chance.

INPUTS:

-1 : --feature_1 : the 'covered' input data file

-2 : --feature_2 : the 'covering' input data file

Input files are accepted in either the wiggle or bed format. If the filename

extension is .wig, the wiggle file is parsed. Otherwise, the file is assumed

to be a bed file. For asymmetric stats, the covered file is what the numerator

in the test statistic should be. ie, for fold enrichment, the stat is the

quotient covering_region/covered_region. Details are in the doc strings.

-d : --domain : the domain of the data files, the portion of the genome over

which these features (-1 and -2) are defined. The support of the statistics.

Usually determined by array coverage or "alignibility". If the features are

defined everywhere (e.g. such as may be the case in C. elegans data), then

this file contains one line for each chromosome, with: "chr_name 0 chr_length"

on each line.

The file formats are described in the README under INPUT FILE FORMATS.

-r --region_fraction : the fraction of each region (e.g. chromosome) to take

in each block-wise sample. The product of this number, and the minimum

segment length (e.g. for no segmentation, using whole human chromosomes, the

minimum segment length would be about 50Mb due to chromosome Y), should be

larger than the mixing distance of features -1 and -2. For human, if the

minimum segment length is around 5Mb (some segmentation has been done, or the

features of interest are not defined everywhere throughout the genome, e.g.

due to mappability issues), then this number should be at least 0.01 for most

features. This number can be fitted under a stability criterion.

-s --subregion_fraction: only used for the double bootstrap tests,

this specifies the fraction of the subsample to take. For instance, a

specified region fraction (-r) of 0.20 and a subregion fraction (-s) of 0.20

would yield a net sample length of 0.04 of each named region.

-n --num_samples : the number of bootstrap samples to take. 100 will be

sufficient to get an idea of significance, but at least 10K should be used

for publication.

-t --test: Determine the test to run. Accepts one of the following types

basepair_overlap_conditional ( bc ) ( default )

basepair_overlap_marginal ( bm )

region_overlap_conditional ( rc )

region_overlap_marginal ( rm )

pearson_correlation_conditional ( cc )

fold_enrichment_conditional ( fc )

The particulars of the tests are discussed in the code ( docstrings ).

-fg --filter_covering: Filter covering bed file by a group name.

-fd --filter_covered : Filter covered bed file by a group name. In the bed4

format, the 4th column stores a label for the given region. When one of the

above two options are chosen, the file will filter out regions that dont

match 'name' as given. Defaults to no filtering.

-B --force_binary: Treat the input data as binary regardless of whether or not

there is a value. This is useful for bed files which have value's for the

purpose of plotting at the UCSC genome browser ( ie all 1000 ).

--min_overlap_fraction: This is only used for the region overlap test. If the

covered region isn't overlapped by at least (X*100)% of the covering region,

then we dont consider this an overlap. Defaults to 0.0. That is, if the covered

region is overlapped by even 1 basepair, then we say it is covered.

-o --output_file: A file to output all non-error output into. Will append to

this file if it already exists. defaults to standard out.

try:

long_args = [ "help", "feature-1=", "feature-2=", "domain=",

"region-fraction=", "subregion-fraction=",

"num-samples=", "test=", "output-file=",

"filter-covering=", "filter-covered=", "force-binary", "verbose", "min-overlap-fraction=" ]

opts, args = getopt.getopt(sys.argv[1:], "1:2:d:r:s:n:t:o:fg:fd:Bv", long_args)

except getopt.GetoptError, err:

usage()

print "Error: \n", str(err)

print

sys.exit(2)

## make percent_basepair_overlap the default test type

test_type = 'bc'

filter_covering = None

filter_covered = None

force_binary = False

min_overlap_fraction = 0.0

for o, a in opts:

if o == "-v":

global verbose

verbose = True

base_types.verbose = True

elif o in ("-h", "--help"):

usage()

sys.exit()

elif o in ("-1", "--feature-1"):

covered_file = open(a)

elif o in ("-2", "--feature-2"):

covering_file = open(a)

elif o in ("-d", "--domain"):

lengths_file = open(a)

elif o in ("-r", "--region-fraction"):

region_fraction = float(a)

elif o in ("-s", "--subregion-fraction"):

subregion_fraction = float(a)

elif o in ("-n", "--num-samples"):

num_samples = int(a)

elif o in ("-t", "--test"):

test_type = a

elif o in ("-B", "--force-binary"):

force_binary = True

elif o in ("-o", "--output_file"):

global output

output = open(a, 'a')

elif o in ("-fg", "--filter-covering"):

filter_covering = a

elif o in ("-fd", "--filter-covered"):

filter_covered = a

elif o in ("--min-overlap-fraction", ):

min_overlap_fraction = float( a )

if min_overlap_fraction < 0 or min_overlap_fraction > 1.0:

raise ValueError, "min_overlap_fraction must be between 0 and 1.0, inclusive."

else:

assert False, "unhandled option %s" % o

try: covered_file, covering_file, lengths_file, region_fraction, num_samples

except Exception, inst:

usage()

sys.exit()

if verbose:

import time

startTime = time.time()

if covered_file.name.endswith(".wig"):

coveredAnnotations = parse_wiggle_file(covered_file, lengths_file, force_binary)

else:

coveredAnnotations = parse_bed_file(covered_file, lengths_file, filter_covered, force_binary)

if covering_file.name.endswith(".wig"):

coveringAnnotations = parse_wiggle_file(covering_file, lengths_file, force_binary)

else:

coveringAnnotations = parse_bed_file(covering_file, lengths_file, filter_covering, force_binary)

covered_file.close()

covering_file.close()

lengths_file.close()

if verbose:

print "Input Files Parse Time: ", time.time() - startTime, "\n"

startTime = time.time()

if not vars().has_key('region_fraction'):

raise ValueError, 'The region_fraction setting is not set - it is mandatory.'

#### Tests that work as for continuous data *or* for binary data

if test_type in ( 'pearson_correlation_conditional', 'cc' ):

conditional_pearson_correlation(

coveringAnnotations, coveredAnnotations,

region_fraction, num_samples

)

elif test_type in ( 'fold_enrichment_conditional', 'fc' ):

if not vars().has_key('subregion_fraction'):

raise ValueError, 'The fold_enrichment_conditional test requires that the subregion_fraction option be set.'

conditional_mean_fold_enrichment(

coveringAnnotations, coveredAnnotations,

region_fraction, subregion_fraction, num_samples

)

else:

#### Tests that ONLY work for binary data

# test for the correct object

if type(coveredAnnotations.values()[0]) != binary_region \

or type(coveringAnnotations.values()[0]) != binary_region:

raise TypeError, "The %s test only works for binary data ( You can force this with the -B option )" % test_type

if test_type in ( 'basepair_overlap_marginal', 'bm' ) :

marginal_bp_overlap_stat( coveringAnnotations, coveredAnnotations,

region_fraction, num_samples )

elif test_type in ( 'basepair_overlap_conditional', 'bc' ) :

conditional_bp_overlap_stat( coveringAnnotations, coveredAnnotations,

region_fraction, num_samples )

elif test_type in ( 'region_overlap_marginal', 'rm' ):

if not vars().has_key('subregion_fraction'):

raise ValueError, 'The region_overlap_marginal test requires that the subregion_fraction option be set.'

marginal_resample_region_overlap_stat(

coveringAnnotations, coveredAnnotations,

region_fraction, subregion_fraction,

num_samples, min_overlap_fraction

)

elif test_type in ( 'region_overlap_conditional', 'rc' ):

if not vars().has_key('subregion_fraction'):

raise ValueError, 'The region_overlap_conditional test requires that the subregion_fraction option be set.'

conditional_resample_region_overlap_stat(

coveringAnnotations, coveredAnnotations,

region_fraction, subregion_fraction,

num_samples, min_overlap_fraction

)

else:

raise ValueError, "Unrecognized test '%s'" % test_type

if verbose: print >> output, "\nExecution Time: ", time.time()-startTime